System and method for determining common patterns in multimedia content elements based on key points

ABSTRACT

A system and method for method for determining common patterns based on key points in multimedia data elements (MMDEs). The method includes: identifying a plurality of candidate key points in each of the plurality of MMDEs, wherein a size of each candidate key point is equal to a predetermined size and a scale of each candidate key point is equal to a predetermined scale; analyzing the identified candidate key points to determine a set of properties for each candidate key point; comparing the sets of properties of the plurality of candidate key points of each MMDE; selecting, for each MMDE, a plurality of key points from among the candidate key points based on the comparison; generating, based on the key points for each MMDE, a signature for the MMDE; and comparing the signatures of the plurality of MMDEs to output at least one common pattern among the plurality of MMDEs.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/307,511 filed on Mar. 13, 2016. This application is also acontinuation-in-part of U.S. patent application Ser. No. 15/336,218filed on Oct. 27, 2016, now pending, which claims the benefit of U.S.Provisional Application No. 62/267,398 filed on Dec. 15, 2015. Thecontents of the above-mentioned applications are hereby incorporated byreference.

TECHNICAL FIELD

The present disclosure relates generally to analyzing multimediacontent, and particularly to utilization of key points in multimediadata elements.

BACKGROUND

With the abundance of multimedia data made available through variousmeans in general and the Internet and world-wide web (WWW) inparticular, there is a need for effective ways of analyzing andidentifying such multimedia data. Such multimedia data may include, forexample, images, graphics, video streams, video clips, video frames,photographs, images of signals, and the like.

Analyzing such multimedia content may be challenging at best due to thehuge amount of information that needs to be examined. Helpful and vitaldata analysis becomes time intensive due to the amount of data that mustbe processed. As a result, data analysis may be a low priority orignored entirely.

Even identifying multimedia content elements included in multimediacontent is a challenging problem. Existing solutions may includeprocessing, analyzing, and understanding the multimedia content based onone or more decisions. A theme of development for many such existingsolutions has been to replicate the abilities of human vision byelectronically perceiving and recognizing multimedia content items.

The existing solutions are limited in the ability to identify multimediacontent elements that are received for the first time. In particular,many existing solutions require comparing portions of multimedia contentto known multimedia content elements to identify any matching multimediacontent elements. Thus, unknown multimedia content elements ormultimedia content elements that have otherwise never been receivedbefore may not be successfully recognized.

Additionally, existing solutions are often highly sensitive to changesin the received multimedia content elements. Consequently, minor changesin the multimedia content due to, for example, differences duringcapturing, may result in otherwise identical multimedia content elementsbeing unrecognizable. For example, taking pictures of a car at differentangles (e.g., one from the rear right side and another from the frontleft side) may result in the car being unrecognizable by existingsolutions for one or more of the pictures.

Other existing solutions rely on metadata to identify multimedia contentelements. Use of such metadata typically relies on information from,e.g., users. Thus, the metadata may not be sufficiently defined to fullydescribe the multimedia content and, as a result, may not capture allaspects of the multimedia content. For example, a picture of a car maybe associated with metadata representing a model of the car, but otherpictures of the car may not be associated with metadata designating theowners.

Further, the existing solutions often include analyzing each and everypixel of the multimedia content and matching those pixels to pixels in adatabase. This analysis consumes a significant amount of computingresources. Further, such a complete analysis may be unnecessary, asportions of multimedia content may not contribute to the identificationof elements therein. For example, images may include black edges whichare not useful for identification. As another example, text that isrepeated throughout an image may only be useful to identification once,and subsequent analysis of the text is not useful.

It would therefore be advantageous to provide a solution that wouldovercome the deficiencies of the prior art.

SUMMARY

A summary of several example embodiments of the disclosure follows. Thissummary is provided for the convenience of the reader to provide a basicunderstanding of such embodiments and does not wholly define the breadthof the disclosure. This summary is not an extensive overview of allcontemplated embodiments, and is intended to neither identify key orcritical elements of all embodiments nor to delineate the scope of anyor all aspects. Its sole purpose is to present some concepts of one ormore embodiments in a simplified form as a prelude to the more detaileddescription that is presented later. For convenience, the term “someembodiments” may be used herein to refer to a single embodiment ormultiple embodiments of the disclosure.

Certain embodiments disclosed herein include a method for determiningcommon patterns in multimedia data elements (MMDEs) based on key points.The method comprises: identifying a plurality of candidate key points ineach of the plurality of MMDEs, wherein a size of each candidate keypoint is equal to a predetermined size and a scale of each candidate keypoint is equal to a predetermined scale; analyzing the identifiedcandidate key points to determine a set of properties for each candidatekey point; comparing the sets of properties of the plurality ofcandidate key points of each MMDE; selecting, for each MMDE, a pluralityof key points from among the candidate key points based on thecomparison; generating, based on the plurality of key points for eachMMDE, a signature for the MMDE; and comparing the signatures of theplurality of MMDEs to output at least one common pattern among theplurality of MMDEs.

Certain embodiments disclosed herein also include a non-transitorycomputer readable medium having stored thereon instructions for causingone or more processing units to execute a method, the method comprising:identifying a plurality of candidate key points in each of the pluralityof MMDEs, wherein a size of each candidate key point is equal to apredetermined size and a scale of each candidate key point is equal to apredetermined scale; analyzing the identified candidate key points todetermine a set of properties for each candidate key point; comparingthe sets of properties of the plurality of candidate key points of eachMMDE; selecting, for each MMDE, a plurality of key points from among thecandidate key points based on the comparison; generating, based on theplurality of key points for each MMDE, a signature for the MMDE; andcomparing the signatures of the plurality of MMDEs to output at leastone common pattern among the plurality of MMDEs.

Certain embodiments disclosed herein also include a system fordetermining common patterns in multimedia data elements (MMDEs) based onkey points. The system comprises: a processing circuitry; and a memory,the memory containing instructions that, when executed by the processingcircuitry, configure the system to: identify a plurality of candidatekey points in each of the plurality of MMDEs, wherein a size of eachcandidate key point is equal to a predetermined size and a scale of eachcandidate key point is equal to a predetermined scale; analyze theidentified candidate key points to determine a set of properties foreach candidate key point; compare the sets of properties of theplurality of candidate key points of each MMDE; select, for each MMDE, aplurality of key points from among the candidate key points based on thecomparison; generate, based on the plurality of key points for eachMMDE, a signature for the MMDE; and compare the signatures of theplurality of MMDEs to output at least one common pattern among theplurality of MMDEs.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed herein is particularly pointed out anddistinctly claimed in the claims at the conclusion of the specification.The foregoing and other objects, features, and advantages of thedisclosed embodiments will be apparent from the following detaileddescription taken in conjunction with the accompanying drawings.

FIG. 1 is a schematic diagram of a system for identifying key points inmultimedia data elements according to an embodiment.

FIG. 2 is a schematic diagram of a properties generation unit accordingto an embodiment.

FIG. 3 is a flowchart illustrating a method for identifying key pointsin multimedia data elements according to an embodiment.

FIG. 4 is a flowchart illustrating a method for generating propertiesbased on candidate key points according to an embodiment.

FIG. 5 is a flow chart illustrating a method for selecting key pointsfrom among candidate key points according to an embodiment.

FIG. 6 is an example simulation of identifying key points in an imagemultimedia data element.

FIG. 7 is an example data plot utilized to illustrate determining alocation property of a candidate key point.

FIG. 8 is an example simulation of determining a rotation property of acandidate key point.

FIG. 9 is an example data plot utilized to illustrate determining a sizeproperty of a candidate key point.

FIG. 10 is an example data plot utilized to illustrate determining apixilation property of a candidate key point.

FIG. 11 is an example data plot utilized to illustrate determining a keypoint based on an analysis of a set of properties of each of a pluralityof candidate key points.

FIG. 12 is a flowchart illustrating a method for determining commonpatterns in multimedia data elements based on identified key pointsaccording to an embodiment.

FIG. 13 is a block diagram depicting the basic flow of information in asignature generator system.

FIG. 14 is a diagram showing the flow of patches generation, responsevector generation, and signature generation in a large-scalespeech-to-text system.

DETAILED DESCRIPTION

It is important to note that the embodiments disclosed herein are onlyexamples of the many advantageous uses of the innovative teachingsherein. In general, statements made in the specification of the presentapplication do not necessarily limit any of the various claimedembodiments. Moreover, some statements may apply to some inventivefeatures but not to others. In general, unless otherwise indicated,singular elements may be in plural and vice versa with no loss ofgenerality. In the drawings, like numerals refer to like parts throughseveral views.

The various disclosed embodiments include a method and system fordetermining common patterns in multimedia data elements based on keypoints in the multimedia data elements. The identified key points may beutilized to identify multimedia content elements in the multimedia dataelements. A multimedia data element is analyzed to identify candidatekey points. The candidate key points are analyzed to determine a set ofproperties for each candidate key point. Key points are selected basedon the determined sets of properties. Signatures are generated for theselected key points. In some embodiments, the generated signatures maybe clustered into a cluster representing the multimedia data element.The signatures or cluster of the multimedia data element may be comparedto signatures of other multimedia data elements to output commonpatterns among the multimedia data elements.

FIG. 1 shows an example schematic diagram of a system 100 fordetermining common patterns based on key points in multimedia dataelements (MMDEs) according to an embodiment. The system 100 includes aninterface 110, a processing circuitry 120, a memory 130, a propertiesgenerator (PG) 140, a storage 150, and a signature generator 160. Thememory 130 may be volatile (e.g., RAM, etc.), non-volatile (e.g., ROM,flash memory, etc.), or a combination thereof.

Key points are areas within an MMDE of predetermined size and scale thatare determined by the system 100 to be the best representations ofelements shown in the MMDE. A key point is an area of interest withinthe MMDE. Key points can be utilized to allow for efficientidentification of elements shown in the MMDE by, for example, computervision systems. Further, the key points may be utilized asrepresentative portions of a MMDE for generating signatures thereto suchthat the signatures are only generated for the representative portions,thereby reducing use of computing resources and improving accuracyduring signature generation.

As an example, for a picture of a cat lying on grass, portions of thepicture in which the cat or part of the cat is shown may be consideredof stronger interest than portions in which only grass is shown. Thus,the area in the picture showing the cat is a key point. As anotherexample, for a picture of a sunset reflected in the ocean, portions ofthe picture in which both the sun and ocean appear may be considered keypoints, while portions featuring only the sun or the ocean may not beconsidered key points.

The key points may be determined based on at least one candidate keypoint identified in an MMDE. In an embodiment, the identified candidatekey points may be selected randomly from among points in the MMDEidentified during the analysis. In another embodiment, the candidate keypoints may be identified based on at least one predetermined key pointrepresentation rule. As a non-limiting example, a key pointrepresentation rule may include a distance threshold such that only oneof any two points having a distance between the two points less than thedistance threshold are is selected as a candidate key point. A key pointrepresentation rule is described herein below.

MMDEs may be received through the interface 110. The interface 110 maybe, but is not limited to, a network interface. As an example, theinterface 110 may be a network interface for receiving MMDEs from one ormore data sources (not shown) over a network (not shown). The datasources may be, for example, servers (e.g., web servers) or othersources of data including MMDEs. Each MMDE may be, but is not limitedto, an image, a graphic, a video stream, a video clip, a video frame, aphotograph, and an image of signals (e.g., spectrograms, phasograms,scalograms, etc.), combinations thereof, and portions thereof.

The properties generator 140 is configured to generate a set ofproperties for each candidate key point. The properties are scalablemeasures enabling evaluation of each candidate key point as well asdetermination of key points from among the candidate key points. Theproperties may include, but are not limited to, a location of acandidate key point within an MMDE, a rotation of a candidate key pointwithin the MMDE, a size of the candidate key point relative to the MMDE,a pixilation of the candidate key point, combinations thereof, and thelike. In an embodiment, the properties generator 140 may be furtherconfigured to identify benchmarking metrics utilized for determiningproperties of the candidate key points. For example, for an image,benchmarking metrics may include a white color against which othercolors in the image may be compared. The benchmarking metrics utilizedand properties determined may be based on a type of the MMDE. Forexample, metrics for an image may differ from metrics for audio.

The location of the candidate key point may be represented in an XYdiagram, wherein the point (0,0) represents one or more edges of theMMDE. The size of a candidate key point is a size of a multimediacontent element. The rotation of the candidate key point is an angle atwhich a multimedia content element located at the candidate key point istilted with respect to a baseline and may be determined respective of,for example, 8 different benchmarking metrics representing differentrotations. The baseline may be further determined based on othermultimedia content elements of the MMDE. The pixilation may berepresented, e.g., in two rectangles (e.g., a 6×3 black rectangle and a3×6 white rectangle).

The properties generator 140 may be further configured to store thegenerated properties in the storage 150. The properties generator 140 isdescribed further herein below with respect to FIG. 2.

The signature generator 160 is configured to generate signatures forMMDEs or portions thereof. Each signature is a distinct numericrepresentation of an MMDE or portion thereof such that differentsignatures represent different features of MMDEs or portions thereof,which may be robust to noise and distortion. The signature generator 160may be further configured to compare a plurality of signatures. Thecomparison may include, but is not limited to, mapping a signaturevector representation into vector space, using Euclidean distance as afirst approximation, and the like. This comparison may be assisted byembedding algorithms which reduce the number of dimensions in the vectorspace. In an embodiment, the signature generator 160 may be configuredto generate signatures as described further herein below with respect toFIGS. 13 and 14.

The processing circuitry 120 is configured to receive one or more MMDEsthrough the interface 110 and to determine candidate key points for eachof the received MMDEs. The processing circuitry 120 is furtherconfigured to cause the properties generator 140 to generate the set ofproperties for each determined candidate key point and to retrieve thegenerated sets of properties. Based on the retrieved properties, theprocessing circuitry 120 is configured to identify key points from amongthe candidate key points.

The processing circuitry 120 is typically coupled to the memory 130. Theprocessing circuitry 120 may comprise or be a component of a processor(not shown) or an array of processors coupled to the memory 130. Thememory 130 contains instructions that can be executed by the processingcircuitry 120. The instructions, when executed by the processingcircuitry 120, cause the processing circuitry 120 to perform the variousfunctions described herein. The one or more processors may beimplemented with any combination of general-purpose microprocessors,multi-core processors, microcontrollers, digital signal processors(DSPs), field programmable gate array (FPGAs), programmable logicdevices (PLDs), controllers, state machines, gated logic, discretehardware components, dedicated hardware finite state machines, or anyother suitable entities that can perform calculations or othermanipulations of information.

The processing circuitry 120 may also include machine-readable media forstoring software. Software shall be construed broadly to mean any typeof instructions, whether referred to as software, firmware, middleware,microcode, hardware description language, or otherwise. Instructions mayinclude code (e.g., in source code format, binary code format,executable code format, or any other suitable format of code). Theinstructions, when executed by the one or more processors, cause theprocessing system to perform the various functions described herein.

In another embodiment, the processing circuitry 120 can be realized asan array of computational cores, each core having properties that are atleast partly statistically independent from other cores of the pluralityof computational cores. The array of computational cores may beinitialized by the signature generator 160 to generate signatures. Suchcores are generated or otherwise configured to obtain maximalindependence, i.e., the projection from a signal space should generate amaximal pair-wise distance between any two cores' projections into ahigh-dimensional space. Further, the cores are optimally designed forthe type of signals, i.e., the cores should be maximally sensitive tothe spatio-temporal structure of the injected signal, for example, andin particular, sensitive to local correlations in time and space. Thus,in some cases a core represents a dynamic system, such as in statespace, phase space, edge of chaos, etc., which is uniquely used hereinto exploit their maximal computational power. In addition, thecomputational cores are optimally designed with regard to invariance toa set of signal distortions, of interest in relevant applications. Adetailed description of processes for generating, configuring, andoperating any array of computational cores is discussed in more detailin U.S. Pat. No. 8,655,801 assigned to the common assignee, which ishereby incorporated by reference for all the useful information itcontains.

It should be understood that the embodiments disclosed herein are notlimited to the specific architecture illustrated in FIG. 1, and thatother architectures may be equally used without departing from the scopeof the disclosed embodiments. Moreover, in an embodiment, there may be aplurality of systems 100 operating as described hereinabove andconfigured to either have one as a standby, to share the load betweenthem, or to split the functions between them.

FIG. 2 is an example flow diagram illustrating image processing by theproperties generator 140 according to an embodiment. In the exampleembodiment shown in FIG. 2, the properties generator 140 includes alocation determination circuit (LDC) 141, a rotation determinationcircuit (RDC) 142, a size determination circuit (SDC) 143, and apixilation determination circuit (PDC) 144. In an optionalimplementation, the properties generator 140 also includes a comparator145. Further, in the example embodiment shown in FIG. 2, a MMDE isprocessed in the following order: by the location determination circuit141, by the rotation determination circuit 142, by the sizedetermination circuit 143, by the pixilation determination circuit 144,and by the comparator 145.

The location determination circuit 141 is configured to determine alocation of a candidate key point in a MMDE. The location may berelative to the MMDE. To this end, when the MMDE is an image, thelocation may be expressed as a pair of, e.g., X and Y coordinates (X,Y).The origin (0,0) may be any point in the MMDE. As a non-limitingexample, the origin may be at the bottom left corner of the MMDE suchthat points in the MMDE are at coordinates (0,0), (100,150), and anycoordinates in between such as, but not limited to, (0,90), (50,0),(75,75), (99,149), (80,120), and so on. When the MMDE is audio, thelocation may be expressed as a moment or period of time in the audiofile. For example, for an audio clip that is 5 minutes (300 seconds)long, the location may be, but is not limited to, 0 seconds (start), 30seconds, 100 seconds, 267 seconds, 150.2 seconds, 300 seconds (end), andthe like.

The rotation determination circuit 142 is configured to determine arotation of candidate key points in MMDEs. As a non-limiting example,the rotation determination circuit 142 is configured to identify edgesof MMDEs, thereby enabling matching of the rotation of the MMDEs basedon the respective edges thereof. The size determination circuit 143 isconfigured to determine a size of candidate key points in MMDEs. Thepixilation determination circuit is configured to 144 determines apixilation of candidate key points in MMDEs. As a non-limiting example,in an image of a couple hugging in front of the Eiffel tower, thepixilation of the portion of the image showing the couple is higher thanthe pixilation of the portion of the image showing the Eiffel tower inthe background. Each of the location determination circuit 141, therotation determination circuit 142, the size determination circuit isconfigured to 143, and the pixilation determination circuit 144 maydetermine its respective properties based on characteristics of elementsin the MMDE. To this end, each circuit, 142, 143, or 144 may beconfigured to identify at least one benchmarking metric based on theMMDE and to compare elements in the MMDE to the benchmarking metric.

Each benchmarking metric may be a metric representing a particularrotation, size, or pixilation of an MMDE, and may be utilized as a pointof comparison by, for example, the rotation determination circuit 142,the size determination circuit 143, or the pixilation determinationcircuit 144, respectively. To this end, each of the rotation, size, andpixilation of a MMDE may be determined relative to at least onecorresponding benchmarking metric. As a non-limiting example, if a textelement in an image is identified as being in a particular character set(e.g., letters of the English alphabet), the rotation determinationcircuit 142 may be configured to determine a rotation of the textelement with respect to a benchmarking metric text element in the samecharacter set (e.g., a horizontally oriented text element using Englishalphabet letters).

In an embodiment, the properties generator 140 may include a comparisonunit 145. The comparison unit 145 compares a set of properties of eachcandidate key point. The comparison may be utilized to determine whethera candidate key point should be selected as a key point. To this end,the comparator 145 may be configured to compare scores of properties ofthe same type (e.g., scores for locations of different candidate keypoints, scores for rotations of different candidate key points, and thelike).

In an embodiment, each, some, or all of the location determinationcircuit 141, the rotation determination circuit 142, the sizedetermination circuit 143, the pixilation determination circuit (PDC)144, and the comparator 145 may comprise or be a component of aprocessor (not shown) or an array of processors. Examples for suchprocessor or processors are provided above.

It should be noted that the flow diagram shown in FIG. 2 is merely anexample and does not limit any of the disclosed embodiments. Inparticular, a MMDE may be processed by any of the location determinationcircuit 141, the rotation determination circuit 142, the sizedetermination circuit 143, and the pixilation determination circuit 144,either in series or in parallel, and may be processed by each circuit inany order. As a non-limiting example, the MMDE may be processed by thelocation determination circuit 141, the rotation determination circuit142, the size determination circuit 143, and the pixilationdetermination circuit 144 simultaneously. As another non-limitingexample, the MMDE may be processed by the circuits in the followingorder: by the rotation determination circuit 142, by the pixilationdetermination circuit 144, by the size determination circuit 143, and bythe location determination circuit 141. Additionally, other circuits fordetermining properties of MMDEs (not shown), such as a colordetermination circuit, may be equally used in addition to or instead ofany of the circuits 141, 142, 143, or 144.

FIG. 3 is an example flowchart 300 illustrating a method for identifyingkey points in a MMDE according to an embodiment. In an embodiment, themethod may be performed by the system 100.

At S310, a MMDE is received. The MMDE may be received via an interface(e.g., the interface 110).

At S320 the MMDE is analyzed to identify candidate key points. In anembodiment, S320 may include image-based recognition of the MMDE. In afurther embodiment, the image-based recognition may begin at the edgesof the MMDE and continue to the center. As an example, if the MMDE is animage, the analysis may begin at the outermost points in the image. Asanother example, if the MMDE is audio, the analysis may begin at thebeginning and end times for the audio.

In an embodiment, the identified candidate key points may be selectedrandomly from among points in the MMDE identified during the analysis.In another embodiment, the candidate key points may be identified basedon at least one predetermined key point representation rule. As anexample, a key point representation rule may include a distancethreshold (e.g., a distance between points in an image or video, alength of time in audio, etc.). If two points in an MMDE are separatedby a distance less than the distance threshold, only one of the pointsmay be identified as a candidate key point.

At S330, a set of properties is determined for each identified candidatekey point. Determination of properties for candidate key points isdescribed further herein below with respect to FIG. 4.

At S340, the properties for each candidate key point are compared. In anembodiment, comparing the properties further includes determining aproperty score for each property of each candidate key point. Theproperty scores may be determined based on comparison of characteristicsof each property such as, but not limited to, intensity, distance from acenter point of the MMDE, color, angle of rotation, a combinationthereof, and the like. The property scores may be determined furtherbased on benchmarking metrics for such characteristics. In anembodiment, higher property scores indicating a greater likelysignificance of the candidate key point. As an example, the locationscores for a particular candidate point may be 3, 7, and 8,respectively, with 1 representing the lowest likelihood of significance(e.g., toward the outer edges of the MMDE) and 10 representing thehighest likelihood of significance (e.g., closest to the center of theMMDE).

At S350, key points are selected from among the identified candidate keypoints. The key points may be selected based on the determined sets ofproperties via, e.g., comparison of the properties' respective scores.Selecting key points among candidate key points is described furtherherein below with respect to FIG. 5.

At optional S360, it is checked whether additional key points arerequired and, if so, execution continues with S320; otherwise, executionterminates. In an embodiment, upon selecting a key point in a particulararea of the MMDE, additional key points may be checked for within, or inproximity to, the area of the key point.

FIG. 4 is an example flowchart S340 illustrating a method fordetermining a set of properties for candidate key points in an MMDEaccording to an embodiment. In an embodiment, each of the properties maybe determined by comparing benchmarking metrics to one or morecharacteristics of the candidate key point.

At S410, a location of a candidate key point is determined. The locationof the candidate key point may be determined by identifying a centerpoint of the MMDE and determining a distance from the center point tothe candidate key point. At S420, a rotation of the candidate key pointmay be determined. The rotation may be determined based on edgesidentified in the MMDE. At S430, a size of the candidate key point maybe determined. At S440, a pixilation of the candidate key point may bedetermined.

At S450, it may be determined whether properties of additional candidatekey points are required and, if so, execution continues with S410;otherwise execution terminates. In an embodiment, the determination maybe based on an MMDE identification rule. The MMDE identification ruleindicates at least one condition for successful identification ofmultimedia content elements and may be based on, but not limited to, anevent (e.g., identification of a concept related to the MMDE), athreshold (e.g., a number of sets of properties for candidate keypoints), a combination thereof, and the like. To this end, in anembodiment in which identification of a concept related to the MMDE isindicated by the MMDE identification rule, S450 may further includedetermining whether a concept can be identified based on the propertiesdetermined thus far.

It should be noted that FIG. 4 is described herein above with respect tolocation, rotation, size, and pixilation properties merely forsimplicity purposes and without limitation on the disclosed embodiments.More, fewer, or other properties may be utilized without departing fromthe scope of the disclosure. As an example, a color property for animage may also be determined. As another example, a volume property foran audio file may be determined, and the rotation and pixilationproperties may not be determined for the audio file.

FIG. 5 is an example flowchart S350 illustrating a method for selectingkey points in a MMDE according to an embodiment.

At S510, sets of properties of candidate key points in the MMDE areobtained. The sets of properties for each candidate key point mayinclude the properties determined as described herein above with respectto FIG. 4.

At S520, the sets of properties are compared to identify relatively highsets of properties. The relatively high sets of properties areidentified to determine the most descriptive candidate key points. In anembodiment, S520 includes determining a property score for eachproperty. Each property score may be determined based on relative valuesfor properties of the candidate key points. In a further embodiment,S520 may also include determining an average property score forproperties of each set of properties. In yet a further embodiment,relatively high sets of properties may be sets of properties havingaverage property scores above a predetermined threshold.

At optional S530, at least one budget parameter may be retrieved. Thebudget parameter is a quantitative limitation on the maximum amount ofkey points that may be selected for the MMDE and is typically utilizedto ensure efficient key point identification by restricting the numberof key points that need to be identified, thereby conserving computingresources. The budget may be the same for all MMDEs, may differ fordifferent types of MMDEs, and the like. In an embodiment, the budget maybe retrieved from the storage unit 150.

At S540, key points to be selected are determined based on thecomparison. The number of key points determined may be limited based onthe budget.

FIG. 6 is an example simulation of a selection of candidate key pointsin a MMDE. In the example simulation of FIG. 6, an image 600 includes acat. The image 600 may be received via an interface and analyzed. Basedon the analysis, candidate key points 610-1 through 610-4 areidentified. A set of properties is generated for each of the candidatekey points 610. The sets of properties are compared. For example, thelocation property of candidate key point 610-1 is relatively low ascompared to candidate key point 610-3 because it is closer to the centerof the image 600. Additionally, the pixilation property of candidate keypoint 610-2 may be relatively low as compared to candidate key point610-4.

FIG. 7 is an example data plot 700 utilized to illustrate determining alocation property of a candidate key point. The example data plot 700includes a set of data points 710 and a subset of data points 720 amongthe set of data points 710. Location properties are determined forcandidate key points. The location properties may be displayed in thedata plot 700 as an XY graph where the data point (0,0) represents apoint on an edge of a MMDE and where the points farthest from the datapoint (0,0) represent the points closest to the center of the MMDE. Inthe example data plot 700, each point (x,y) represents, e.g., ahorizontal and vertical distance, respectively, of the candidate keypoint to the center of the MMDE when the MMDE is an image. In otherexample data plots, each point (x,y) may represent, e.g., a horizontaland vertical distance, respectively, of the candidate key point to oneor more of the edges of the image MMDE. For other types of multimediacontent elements (e.g., video, audio, etc.), each of the X-axis and theY-axis may represent metrics such as, but not limited to, amount of timefrom half of the total time of the audio or video, a horizontal orvertical distance to or from a center of an image-based portion of thevideo, a distance of a line to or from the center of the image-basedportion of the video, and the like.

The candidate key points with the strongest responses (i.e., locationproperties) may be selected. The strongest response key points may bedetermined by comparing the location properties among the candidate keypoints 710 and assigning a location score to each of the candidate keypoints 710. As an example, the strongest response candidate key pointsare associated with points of the subset 720.

FIG. 8 is an example simulation 800 of determining a rotation propertyof a candidate key point. The simulation 800 is based on an image 810 ofa superhero character standing in front of a city background andincludes candidate key points 820 and 830. A rotation property may bedetermined for each of the candidate key points 820 and 830. Thedetermination may include comparing rotations among the candidate keypoints and determining a rotation scale based on the comparison. Thedetermination may further include analyzing the rotation of eachcandidate key point respective of the rotation scale and assigning arotation score to each candidate key point. As an example, the candidatekey point 820 may be assigned a lower rotation score than that ofcandidate key point 830 because the rotation of the candidate key point820 (i.e., a point on the side of a vertically oriented building) isless than the rotation of the candidate key point 830 (i.e., a point onan angle of a bent elbow).

FIG. 9 is an example data plot 900 utilized to illustrate determiningsize properties of candidate key points. The example data plot 900includes key points 910 and a data point cluster 920. Each of the datapoints 910 represents a size property of a candidate key point, with thedata point (0,0) representing the lowest size property and the datapoints of the key point cluster 920 representing the highest sizeproperties among candidate key points. Thus, the largest candidate keypoints are associated with a higher size score and are the candidate keypoints associated with the data point cluster 920.

FIG. 10 is an example data plot 1000 utilized to illustrate determiningpixilation properties of candidate key points. The pixilation propertiesrepresent visual quality of the MMCE as determined based on, e.g.,resolution. The example data plot 1000 includes key points 1010 and adata point cluster 1020 where the data point (0,0) represents the lowestpixilation among candidate key points and the candidate key pointshaving the highest pixilation are represented by data points of thecluster 1020. Key points with the higher pixilation are assigned with ahigher pixilation score.

FIG. 11 is an example data plot 1100 utilized to illustrate selectingkey points based on an analysis of a set of properties of each of aplurality of candidate key points. The example data plot 1100 includespoints 1110 and 1120. Each point 1110 and 1120 represents a candidatekey point having a set of properties. In an example implementation, theset of properties may include 2 or 4 properties. The set of propertiesfor each candidate key point is analyzed to determine which candidatekey points are to be utilized as key points. As an example, thecandidate key point may be assigned da score for each property, and oneor more candidate key points having a highest average score from among aplurality of candidate key points may be selected as key points.

FIG. 12 is an example flowchart 1200 illustrating a method fordetermining common patterns in MMDEs based on key points identifiedtherein according to an embodiment. In an embodiment, the method may beperformed by the system 100, FIG. 1.

At S1210, a plurality of MMDEs is obtained. The plurality of MMDEs maybe received (e.g., from a user device), retrieved (e.g., from astorage), or a combination thereof.

At S1220, a plurality of key points in each obtained MMDE is identified.In an embodiment, the plurality of key points for each MMDE isidentified as described further herein above with respect to FIGS. 3through 5.

At S1230, based on the identified key points, a signature is generatedfor each of the plurality of MMDEs. The signature for each MMDE is adistinct numeric representation of the MMDE, and may be robust to noiseand distortion. In an embodiment, generating the signature for each MMDEmay include generating a signature for each key point in the MMDE andclustering the generated key point signatures to create the signature ofthe MMDE. Such key point-based signature generation results in onlygenerating signatures for select portions of the MMDE that bestrepresent the MMDE and, therefore, may reduce use of computing resourcesfor signature generation, result in more accurate signatures (i.e.,signatures that better represent unique aspects of the MMDE), or both.

In an embodiment, each signature may be generated by a signaturegenerator system. The signature generator system includes a plurality ofat least partially statistically independent computational cores,wherein the properties of each computational core are set independentlyof the properties of the other cores. A detailed description of such asignature generation process is provided in the above-mentioned U.S.Pat. No. 8,655,801.

At S1240, based on the generated signatures, at least one common patternamong the plurality of multimedia content elements is output. In anembodiment, S1240 includes matching between signatures of the pluralityof multimedia content elements to identify portions of at least twosignatures matching above a predetermined threshold. The matchingidentified portions of signatures may be utilized to generate the atleast one common pattern. The at least one common pattern may be or mayinclude a signature representing common features of two or more MMDEs.To this end, in a further embodiment, S1240 may include selecting arepresentative signature from among two or more matching portions ofsignatures to be utilized as a common pattern.

The determined common patterns may be hidden patterns, i.e., patternsthat are not revealed using other methods (e.g., based on comparison ofmetadata associated with the MMDEs). The determined common patterns maybe utilized to, e.g., organize the plurality of MMDEs, generatesimilarity statistics, identify anomalies, and the like. For example,the MMDEs may be grouped with respect to the determined common patterns.

FIGS. 13 and 14 illustrate an example for generation of signatures forthe multimedia content elements according to an embodiment. An examplehigh-level description of the process for large scale matching isdepicted in FIG. 13. In this example, the matching is for a videocontent.

Video content segments 2 from a Master database (DB) 6 and a Target DB 1are processed in parallel by a large number of independent computationalCores 3 that constitute an architecture for generating the Signatures(hereinafter the “Architecture”). Further details on the computationalCores generation are provided below. The independent Cores 3 generate adatabase of Robust Signatures and Signatures 4 for Targetcontent-segments 5 and a database of Robust Signatures and Signatures 7for Master content-segments 8. An exemplary and non-limiting process ofsignature generation for an audio component is shown in detail in FIG.14. Finally, Target Robust Signatures and/or Signatures are effectivelymatched, by a matching algorithm 9, to Master Robust Signatures and/orSignatures database to find all matches between the two databases.

To demonstrate an example of the signature generation process, it isassumed, merely for the sake of simplicity and without limitation on thegenerality of the disclosed embodiments, that the signatures are basedon a single frame, leading to certain simplification of thecomputational cores generation. The Matching System is extensible forsignatures generation capturing the dynamics in-between the frames.

The Signatures' generation process is now described with reference toFIG. 14. The first step in the process of signatures generation from agiven speech-segment is to breakdown the speech-segment to K patches 14of random length P and random position within the speech segment 12. Thebreakdown is performed by the patch generator component 21. The value ofthe number of patches K, random length P and random position parametersis determined based on optimization, considering the tradeoff betweenaccuracy rate and the number of fast matches required in the flowprocess of the context server 130 and SGS 140. Thereafter, all the Kpatches are injected in parallel into all computational Cores 3 togenerate K response vectors 22, which are fed into a signature generatorsystem 23 to produce a database of Robust Signatures and Signatures 4.

In order to generate Robust Signatures, i.e., Signatures that are robustto additive noise L (where L is an integer equal to or greater than 1)by the Computational Cores 3 a frame T is injected into all the Cores 3.Then, Cores 3 generate two binary response vectors: S which is aSignature vector, and RS which is a Robust Signature vector.

For generation of signatures robust to additive noise, such asWhite-Gaussian-Noise, scratch, etc., but not robust to distortions, suchas crop, shift and rotation, etc., a core Ci={ni} (1≦i≦L) may consist ofa single leaky integrate-to-threshold unit (LTU) node or more nodes. Thenode ni equations are:

$V_{i} = {\sum\limits_{j}{w_{ij}k_{j}}}$ n_(i) = θ(Vi − Th_(x))

where, θ is a Heaviside step function; w_(ij) is a coupling node unit(CNU) between node i and image component j (for example, grayscale valueof a certain pixel j); kj is an image component ‘j’ (for example,grayscale value of a certain pixel j); Thx is a constant Thresholdvalue, where ‘x’ is ‘S’ for Signature and ‘RS’ for Robust Signature; andVi is a Coupling Node Value.

The Threshold values Thx are set differently for Signature generationand for Robust Signature generation. For example, for a certaindistribution of Vi values (for the set of nodes), the thresholds forSignature (Ths) and Robust Signature (ThRs) are set apart, afteroptimization, according to at least one or more of the followingcriteria:

1: For

V_(i)>Th_(RS)

1−p(V>Th_(S))−1−(1−ε)¹<<1

-   -   i.e., given that/nodes (cores) constitute a Robust Signature of        a certain image I, the probability that not all of these I nodes        will belong to the Signature of same, but noisy image, Ĩ is        sufficiently low (according to a system's specified accuracy).

2:

p(V _(i) >Th _(RS))≈l/L

-   -   i.e., approximately/out of the total L nodes can be found to        generate a Robust Signature according to the above definition.    -   3: Both Robust Signature and Signature are generated for certain        frame i.

It should be understood that the generation of a signature isunidirectional, and typically yields lossless compression, where thecharacteristics of the compressed data are maintained but theuncompressed data cannot be reconstructed. Therefore, a signature can beused for the purpose of comparison to another signature without the needof comparison to the original data. The detailed description of theSignature generation can be found in U.S. Pat. Nos. 8,326,775 and8,312,031, assigned to the common assignee, which are herebyincorporated by reference for all the useful information they contain.

A Computational Core generation is a process of definition, selection,and tuning of the parameters of the cores for a certain realization in aspecific system and application. The process is based on several designconsiderations, such as:

-   -   (a) The Cores should be designed so as to obtain maximal        independence, i.e., the projection from a signal space should        generate a maximal pair-wise distance between any two cores'        projections into a high-dimensional space.    -   (b) The Cores should be optimally designed for the type of        signals, i.e., the Cores should be maximally sensitive to the        spatio-temporal structure of the injected signal, for example,        and in particular, sensitive to local correlations in time and        space. Thus, in some cases a core represents a dynamic system,        such as in state space, phase space, edge of chaos, etc., which        is uniquely used herein to exploit their maximal computational        power.    -   (c) The Cores should be optimally designed with regard to        invariance to a set of signal distortions, of interest in        relevant applications.

A detailed description of the Computational Core generation and theprocess for configuring such cores is discussed in more detail in theabove-referenced U.S. Pat. No. 8,655,801.

The various embodiments disclosed herein can be implemented as hardware,firmware, software, or any combination thereof. Moreover, the softwareis preferably implemented as an application program tangibly embodied ona program storage unit or computer readable medium consisting of parts,or of certain devices and/or a combination of devices. The applicationprogram may be uploaded to, and executed by, a machine comprising anysuitable architecture. Preferably, the machine is implemented on acomputer platform having hardware such as one or more central processingunits (“CPUs”), a memory, and input/output interfaces. The computerplatform may also include an operating system and microinstruction code.The various processes and functions described herein may be either partof the microinstruction code or part of the application program, or anycombination thereof, which may be executed by a CPU, whether or not sucha computer or processor is explicitly shown. In addition, various otherperipheral units may be connected to the computer platform such as anadditional data storage unit and a printing unit. Furthermore, anon-transitory computer readable medium is any computer readable mediumexcept for a transitory propagating signal.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the principlesof the disclosed embodiment and the concepts contributed by the inventorto furthering the art, and are to be construed as being withoutlimitation to such specifically recited examples and conditions.Moreover, all statements herein reciting principles, aspects, andembodiments of the disclosed embodiments, as well as specific examplesthereof, are intended to encompass both structural and functionalequivalents thereof. Additionally, it is intended that such equivalentsinclude both currently known equivalents as well as equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure.

What is claimed is:
 1. A method for determining common patterns in aplurality of multimedia data elements (MMDEs), comprising: identifying aplurality of candidate key points in each of the plurality of MMDEs,wherein a size of each candidate key point is equal to a predeterminedsize and a scale of each candidate key point is equal to a predeterminedscale; analyzing the identified candidate key points to determine a setof properties for each candidate key point; comparing the sets ofproperties of the plurality of candidate key points of each MMDE;selecting, for each MMDE, a plurality of key points from among thecandidate key points based on the comparison; generating, based on theplurality of key points for each MMDE, a signature for the MMDE; andcomparing signatures generated for the plurality of MMDEs to output atleast one common pattern among the plurality of MMDEs.
 2. The method ofclaim 1, wherein each common pattern is a signature representingfeatures that are common to at least two of the plurality of MMDEs. 3.The method of claim 2, wherein the at least one common pattern furthercomprises: matching between the generated signatures to determine atleast one set of matching portions of the generated signatures; andgenerating, based on the determined at least one set of matchingportions, the at least one common pattern.
 4. The method of claim 1,wherein generating the signature for each MMDE further comprises:generating a signature to each selected key point of the MMDE; andclustering the generated key point signatures to create the signaturefor the MMDE.
 5. The method of claim 1, further comprising: identifying,in each MMDE, a plurality of points having the predetermined size andthe predetermined scale, wherein the candidate key points are selectedfrom among the identified plurality of points.
 6. The method of claim 5,wherein the candidate key points are selected randomly from among theidentified plurality of points.
 7. The method of claim 5, wherein thecandidate key points are identified based on at least one key pointrepresentation rule.
 8. The method of claim 1, wherein each set ofproperties includes at least one of: location, rotation, size, andpixilation.
 9. The method of claim 1, wherein comparing the sets ofproperties of the plurality of candidate key points further comprises:determining a score for each property of each set of properties for eachcandidate key point, wherein each score is determined by comparing atleast one characteristic of the candidate key point to at least onecharacteristic of each other candidate key point; and determining, basedon the determined scores, the plurality of key points.
 10. Anon-transitory computer readable medium having stored thereoninstructions for causing one or more processing units to execute amethod, the method comprising: identifying, via a computer visionsystem, a plurality of candidate key points in each of the plurality ofMMDEs, wherein a size of each candidate key point is equal to apredetermined size, wherein a scale of each candidate key point is equalto a predetermined scale; analyzing the identified candidate key pointsto determine a set of properties for each candidate key point; comparingthe sets of properties of the plurality of candidate key points of eachMMDE; selecting, for each MMDE, a plurality of key points from among thecandidate key points based on the comparison; generating, based on theplurality of key points for each MMDE, a signature for the MMDE;comparing the signatures of the plurality of MMDEs to output at leastone common pattern among the plurality of MMDEs.
 11. A system foridentifying key points in a multimedia data element (MMDE), comprising:a processing circuitry; and a memory, the memory containing instructionsthat, when executed by the processing circuitry, configure the systemto: identify a plurality of candidate key points in each of theplurality of MMDEs, wherein a size of each candidate key point is equalto a predetermined size, wherein a scale of each candidate key point isequal to a predetermined scale; analyze the identified candidate keypoints to determine a set of properties for each candidate key point;compare the sets of properties of the plurality of candidate key pointsof each MMDE; select, for each MMDE, a plurality of key points fromamong the candidate key points based on the comparison; generate, basedon the plurality of key points for each MMDE, a signature for the MMDE;and compare the signatures of the plurality of MMDEs to output at leastone common pattern among the plurality of MMDEs.
 12. The system of claim11, wherein each common pattern is a signature representing featuresthat are common to at least two of the plurality of MMDEs.
 13. Thesystem of claim 12, wherein the system is further configured to:matching between the generated signatures to determine at least one setof matching portions of the generated signatures; and generate, based onthe determined at least one set of matching portions, the at least onecommon pattern.
 14. The system of claim 11, wherein the system isfurther configured to: generate a signature to each selected key pointof the MMDE; and cluster the generated key point signatures to createthe signature for the MMDE.
 15. The system of claim 11, wherein thesystem is further configured to: identify, in each MMDE, a plurality ofpoints having the predetermined size and the predetermined scale,wherein the candidate key points are selected from among the identifiedplurality of points.
 16. The system of claim 15, wherein the candidatekey points are selected randomly from among the identified plurality ofpoints.
 17. The system of claim 15, wherein the candidate key points areidentified based on at least one key point representation rule.
 18. Thesystem of claim 11, wherein each set of properties includes at least oneof: location, rotation, size, and pixilation.
 19. The system of claim11, wherein the system is further configured to: determine a score foreach property of each set of properties for each candidate key point,wherein each score is determined by comparing at least onecharacteristic of the candidate key point to at least one characteristicof each other candidate key point; and determine, based on thedetermined scores, the plurality of key points.